Machine learning-based seismic response and performance assessment of reinforced concrete buildings

• Published in: Archives of Civil and Mechanical Engineering Vol. 23; no. 2; p. 94
• Main Authors: Kazemi, F., Asgarkhani, N., Jankowski, R.
• Format: Journal Article
• Language: English
• Published: London Springer London 14.03.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Back propagation; Buildings; Civil Engineering; Concrete; Cost analysis; Data points; Datasets; Decision trees; Engineering; Graphical user interface; Limit states; Machine learning; Mechanical Engineering; Methods; Neural networks; Original Article; Performance assessment; Prediction models; Python; Reinforced concrete; Seismic engineering; Seismic response; Structural Materials; Support vector machines; Uniqueness; Seismic limit-state capacity; Machine learning method; Seismic probabilistic assessment; Maximum interstory drift ratio; Predicting seismic performance
• ISSN: 2083-3318; 1644-9665; 2083-3318
• DOI: 10.1007/s43452-023-00631-9

Complexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data points of training dataset for developing data-driven techniques, Incremental Dynamic Analyses (IDAs) were performed considering 165 RC MRFs with two-, to twelve-Story elevations having the bay lengths of 5.0 m, 6.1 m, and 7.6 m assuming near-fault seismic excitations. Then, important structural features were considered in datasets to train and test the ML-based prediction models, which were improved with innovative techniques. The results show that improved algorithms have higher R 2 values for estimating the Maximum Interstory Drift Ratio (IDR max ), and two improved algorithms of artificial neural networks and extreme gradient boosting can estimate the Median of IDA curves (M-IDAs) of RC MRFs, which can be used to estimate the seismic limit-state capacity and performance assessment of existing or newly constructed RC buildings. To validate the generality and accuracy of the proposed ML-based prediction model, a five-Story RC building with different input features was used, and the results are promising. Therefore, graphical user interface is introduced as user-friendly tool to help researchers in estimating the seismic limit-state capacity of RC buildings, while reducing the computational cost and analytical efforts.